Pulse pile-up poses an issue in the study of nuclear reactions and spectroscopy, arising when two pulses overlap, distorting data and compromising the accuracy of energy and timing details. Various digital and analogue techniques have been used to deal with pile-up interference. However, some pile-up events may include interesting pulses that require reconstruction.
This study introduces a...
The field of heavy-ion experiments, such as the future Compressed Baryonic Matter (CBM) experiment at FAIR, necessitates algorithms that are high in performance and efficient in real-time data analysis. The increasing integration of machine learning techniques, particularly artificial neural networks, into physics experiments marks a significant advancement in this domain. The report...
Machine learning is becoming increasingly prevalent in High Energy Physics (HEP), offering significant potential for enhancing trigger and Data Acquisition (DAQ) performance, as well as other real-time control applications. However, the exploration of these techniques in low latency/power Field-Programmable Gate Arrays (FPGAs) is still in its early stages. We introduce hls4ml, a user-friendly...
